Storing semistructured data with STORED
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
When Is ''Nearest Neighbor'' Meaningful?
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Distribution Discovery: Local Analysis of Temporal Rules
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Clustering of streaming time series is meaningless
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
CloseGraph: mining closed frequent graph patterns
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Substructure Clustering on Sequential 3d Object Datasets
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Graph indexing: a frequent structure-based approach
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
GenMax: An Efficient Algorithm for Mining Maximal Frequent Itemsets
Data Mining and Knowledge Discovery
Making Subsequence Time Series Clustering Meaningful
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Comparing Subspace Clusterings
IEEE Transactions on Knowledge and Data Engineering
Clustering graphs by weighted substructure mining
ICML '06 Proceedings of the 23rd international conference on Machine learning
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Summarizing itemset patterns using probabilistic models
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
MARGIN: Maximal Frequent Subgraph Mining
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Detecting time series motifs under uniform scaling
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Transactions on Knowledge Discovery from Data (TKDD)
Towards efficient mining of proportional fault-tolerant frequent itemsets
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Detection of orthogonal concepts in subspaces of high dimensional data
Proceedings of the 18th ACM conference on Information and knowledge management
RING: An Integrated Method for Frequent Representative Subgraph Mining
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Managing and Mining Graph Data
Managing and Mining Graph Data
Subspace Clustering Meets Dense Subgraph Mining: A Synthesis of Two Paradigms
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
RMiCS: a robust approach for mining coherent subgraphs in edge-labeled multi-layer graphs
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
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Subspace clustering is an established mining task for grouping objects that are represented by vector data. By considering subspace projections of the data, the problem of full-space clustering is avoided: objects show no similarity w.r.t. all of their attributes but only w.r.t. subsets of their characteristics. This effect is not limited to vector data but can be observed in several other scientific domains including graphs, where we just find similar subgraphs, or time series, where only shorter subsequences show the same behavior. In each scenario, using the whole representation of the objects for clustering is futile. We need to find clusters of similar substructures. However, none of the existing substructure mining paradigms as subspace clustering, frequent subgraph mining, or motif discovery is able to solve this task entirely since they tackle only a few challenges and are restricted to a specific type of data. In this work, we unify and generalize existing substructure mining tasks to the novel paradigm of substructure clustering that is applicable to data of an arbitrary type. As a proof of concept showing the feasibility of our novel paradigm, we present a specific instantiation for the task of subgraph clustering. By integrating the ideas of different research areas into a novel paradigm, the aim of our paper is to inspire future research directions in the individual areas.